Method, device and equipment for generating wireless network optimization scheme

Abstract

The present disclosure proposes a wireless network optimization scheme generation method, device and equipment, which is executed by a base station. The method comprises: receiving reported data of an external device, wherein the reported data is used to indicate network test data and base station cell data, the network test data is obtained by the external device according to terminal log data, and the base station cell data is obtained by the external device from a network optimization platform according to the network test data; inputting the reported data into a pre-trained wireless network optimization model to obtain a wireless network optimization scheme, and sending the wireless network optimization scheme to the external device. Through the implementation of the method of the present disclosure, the reported data collected by the external device can be processed based on the pre-trained wireless network optimization model on the base station side to obtain the corresponding wireless network optimization scheme, which can effectively improve the intelligent degree of the network optimization process, accurately locate the network problem, and improve the network optimization efficiency.

Description

TECHNICAL FIELD

[0001] The present disclosure relates to the technical field of wireless self-intelligent network optimization, in particular to a wireless network optimization scheme generation method, device and equipment. BACKGROUND

[0002] With the increasing demand for mobile services, the most important thing is to ensure the quality of the wireless network. The quality of the wireless network can directly affect the user's experience, so quickly solving the problem of the wireless network is an important part of ensuring the quality of the wireless network.

[0003] In the related art, when optimizing the wireless network problem, the problem positioning is not accurate and the efficiency is low. SUMMARY

[0004] The present disclosure aims to at least solve one of the technical problems in the related art to some extent.

[0005] To this end, the purpose of the present disclosure is to propose a wireless network optimization scheme generation method, device, electronic equipment and storage medium, which can process the reported data collected by an external device based on a pre-trained wireless network optimization model of a base station to obtain a corresponding wireless network optimization scheme, effectively improving the intelligent degree of the network optimization process, accurately positioning the network problem and improving the network optimization efficiency.

[0006] To achieve the above purpose, the wireless network optimization scheme generation method proposed by the first aspect of the present disclosure is executed by a base station, and the method comprises:

[0007] Receiving reported data of an external device, wherein the reported data is used to indicate network test data and base station cell data, the network test data is obtained by the external device according to the log data of the terminal, and the base station cell data is collected by the external device from the network optimization platform according to the network test data;

[0008] Inputting the reported data into a pre-trained wireless network optimization model to obtain a wireless network optimization scheme, and sending the wireless network optimization scheme to the external device.

[0009] To achieve the above purpose, the wireless network optimization scheme generation method proposed by the second aspect of the present disclosure is executed by an external device, and the method comprises:

[0010] Obtaining log data of a terminal and analyzing the log data to obtain network test data;

[0011] Collecting base station cell data from the network optimization platform according to the network test data;

[0012] According to the network test data and the base station cell data, report data is generated and sent to a base station;

[0013] The wireless network optimization scheme sent by the base station is received, wherein the wireless network optimization scheme is obtained by the base station based on a wireless network optimization model processing the report data.

[0014] To achieve the above-mentioned purpose, the wireless network optimization scheme generation device provided by the third aspect of the present disclosure is executed by a base station, and the device comprises:

[0015] A first receiving module is configured to receive report data of an external device, wherein the report data is used to indicate network test data and base station cell data, the network test data is obtained by the external device according to log data of a terminal, and the base station cell data is obtained by the external device from a network optimization platform according to the network test data.

[0016] A first sending module is configured to input the report data into a pre-trained wireless network optimization model to obtain a wireless network optimization scheme, and send the wireless network optimization scheme to the external device.

[0017] To achieve the above-mentioned purpose, the wireless network optimization scheme generation device provided by the fourth aspect of the present disclosure is executed by an external device, and the device comprises:

[0018] A first obtaining module is configured to obtain log data of a terminal, and parse the log data to obtain network test data.

[0019] A second obtaining module is configured to obtain base station cell data from a network optimization platform according to the network test data.

[0020] A second sending module is configured to generate report data according to the network test data and the base station cell data, and send the report data to a base station.

[0021] A second receiving module is configured to receive a wireless network optimization scheme sent by the base station, wherein the wireless network optimization scheme is obtained by the base station based on a wireless network optimization model processing the report data.

[0022] The electronic device provided by the fifth aspect of the present disclosure comprises a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to realize the wireless network optimization scheme generation method provided by the first aspect of the present disclosure, or realize the wireless network optimization scheme generation method provided by the second aspect of the present disclosure.

[0023] The sixth aspect of the present disclosure provides a non-transitory computer-readable storage medium, which stores a computer program. The computer program is executed by a processor to implement the method for generating a wireless network optimization scheme according to the first aspect of the present disclosure, or to implement the method for generating a wireless network optimization scheme according to the second aspect of the present disclosure.

[0024] The fifth aspect of the present disclosure provides a computer program product. When instructions in the computer program product are executed by a processor, the method for generating a wireless network optimization scheme according to the first aspect of the present disclosure is executed, or the method for generating a wireless network optimization scheme according to the second aspect of the present disclosure is executed.

[0025] The method, device, electronic device and storage medium for generating a wireless network optimization scheme provided by the present disclosure can receive report data of an external device, wherein the report data is used to indicate network test data and base station cell data, the network test data is obtained by the external device according to log data of a terminal, and the base station cell data is obtained by the external device from a network optimization platform according to the network test data; the report data is input into a pre-trained wireless network optimization model to obtain a wireless network optimization scheme, and the wireless network optimization scheme is sent to the external device. By implementing the method of the present disclosure, the report data collected by the external device can be processed based on the pre-trained wireless network optimization model on the base station side to obtain a corresponding wireless network optimization scheme, which can effectively improve the intelligent degree of the network optimization process, accurately locate network problems, and improve the network optimization efficiency.

[0026] Additional aspects and advantages of the present disclosure will be in part apparent and in part pointed out hereinafter. BRIEF DESCRIPTION OF DRAWINGS

[0027] The above and / or additional aspects and advantages of the present disclosure will become apparent and be readily appreciated from the following description, including the accompanying drawings, wherein:

[0028] Figure 1 FIG. 1 is a flowchart of a method for generating a wireless network optimization scheme according to an embodiment of the present disclosure;

[0029] Figure 2 FIG. 2 is a flowchart of a method for generating a wireless network optimization scheme according to another embodiment of the present disclosure;

[0030] Figure 3 FIG. 3 is a flowchart of a method for generating a wireless network optimization scheme according to another embodiment of the present disclosure;

[0031] Figure 4is a flowchart of a wireless network optimization scheme generation method according to an embodiment of the present disclosure;

[0032] Figure 5 is a flowchart of a wireless network optimization scheme recommendation method according to an embodiment of the present disclosure;

[0033] Figure 6 is a structural diagram of a terminal and a base station edge computing node according to an embodiment of the present disclosure;

[0034] Figure 7 is a flowchart of a wireless network optimization model construction and application method according to an embodiment of the present disclosure;

[0035] Figure 8 is a flowchart of a wireless network optimization scheme model training method according to an embodiment of the present disclosure;

[0036] Figure 9 is a structural diagram of a wireless network optimization scheme generation device according to an embodiment of the present disclosure;

[0037] Figure 10 is a structural diagram of a wireless network optimization scheme generation device according to another embodiment of the present disclosure;

[0038] Figure 11 A block diagram of an exemplary electronic device suitable for implementing an embodiment of the present disclosure is shown. DETAILED DESCRIPTION

[0039] Embodiments of the present disclosure are described in detail below with reference to the accompanying drawings, examples of which are shown in the drawings, wherein the same or similar notations represent the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and are only used to explain the present disclosure and cannot be understood as limiting the present disclosure. On the contrary, embodiments of the present disclosure include all changes, modifications and equivalents falling within the spirit and scope of the appended claims.

[0040] Figure 1 is a flowchart of a wireless network optimization scheme generation method according to an embodiment of the present disclosure.

[0041] It should be noted that the execution subject of the wireless network optimization scheme generation method of the present embodiment is a wireless network optimization scheme generation device, which can be implemented by software and / or hardware. The device can be configured in an electronic device, which can include but is not limited to a terminal, a server end, etc. For example, the terminal can be a mobile phone, a palm computer, etc.

[0042] As shown in Figure 1 the wireless network optimization scheme generation method is executed by a base station, and the method comprises:

[0043] S101: receiving reported data of an external device, wherein the reported data is used to indicate network test data and base station cell data, the network test data is obtained by the external device according to log data of a terminal, and the base station cell data is obtained by the external device from a network optimization platform according to the network test data.

[0044] The external device can be a data collection and analysis device configured for a wireless network optimization process. In the embodiment of the disclosure, the external device can establish a communication connection with the terminal, the network optimization platform, the base station and the operation and maintenance center OMC respectively to realize data collection and transmission.

[0045] The reported data can be network optimization related data collected and obtained by the external device from the terminal and the network optimization platform.

[0046] That is to say, in the embodiment of the disclosure, the external device can collect and arrange network optimization related data of the terminal and the network optimization platform, and report to the base station, thereby providing reliable data support for subsequent generation of a wireless network optimization scheme.

[0047] S102: inputting the reported data into a pre-trained wireless network optimization model to obtain a wireless network optimization scheme, and sending the wireless network optimization scheme to the external device.

[0048] The wireless network optimization model can be an artificial intelligence model constructed in advance according to historical network optimization related data, and can be used to analyze the reported data to obtain an applicable wireless network optimization scheme.

[0049] That is to say, in the embodiment of the disclosure, the base station can serve as an edge computing server node and be responsible for model training and inference.

[0050] In the embodiment, the reported data of the external device is received, wherein the reported data is used to indicate network test data and base station cell data, the network test data is obtained by the external device according to log data of a terminal, and the base station cell data is obtained by the external device from a network optimization platform according to the network test data; the reported data is input into a pre-trained wireless network optimization model to obtain a wireless network optimization scheme, and the wireless network optimization scheme is sent to the external device. Through the method of the disclosure, the reported data collected and obtained by the external device can be processed based on the pre-trained wireless network optimization model of the base station side to obtain a corresponding wireless network optimization scheme, which can effectively improve the intelligent degree of the network optimization process, realize accurate positioning of network problems, and improve the network optimization efficiency.

[0051] Optionally, in some embodiments, the network test data comprises a data report and an event report, and the base station cell data at least comprises a neighbor relation table of the mobile communication network, MRO, MDT, a cell performance table, a cell parameter configuration table, a work parameter, an alarm and a historical optimization scheme; wherein,

[0052] The field variable identification of the data report at least comprises: a cell, a time, a longitude, a latitude, a signal receiving power, a signal-to-noise ratio, a frequency point, a MAC layer downlink rate, a MAC layer uplink rate, a downlink modulation and coding strategy, an uplink modulation and coding strategy, a block error rate, a physical cell identification and a voice packet sequence number;

[0053] The field variable identification of the event report at least comprises: a closed inter-frequency measurement A1 event, an open inter-frequency measurement A2 event, a handover A3 event, a handover A5 event based on a main service cell and a neighbor cell threshold, an inter-system handover B1 event, a handover B2 event based on a main service cell and an inter-system neighbor cell threshold, a handover failure, a radio link failure, a voice drop and an RRC connection reestablishment failure.

[0054] Optionally, in some embodiments, the wireless network problem statistics table in the reported data is associated with the MRO, the MDT, the cell performance table, the cell parameter configuration table, the work parameter, the alarm and the historical optimization scheme based on a cell and a time, and the wireless network problem statistics table is obtained by an external device based on a cell and a time for the data report, the event report and the neighbor relation table.

[0055] Figure 2 is a flow diagram of a method for generating a wireless network optimization scheme according to another embodiment of the present disclosure.

[0056] As shown in Figure 2 , the method for generating a wireless network optimization scheme is performed by a base station, and the method comprises:

[0057] S201: receiving a retest result of an external device.

[0058] The retest result can be a result obtained by performing a network quality test on the obtained wireless network optimization scheme, and can be used to indicate whether a wireless network problem corresponding to a terminal is solved.

[0059] That is, the retest can be performed after the wireless network optimization scheme is generated in the embodiment of the present disclosure, and the retest result can be determined by an external device and sent to a base station, thereby providing reliable data support for subsequent adjustment of the base station.

[0060] S202: if the retest result indicates that the wireless network optimization scheme has completed network optimization, taking the network test data, the base station cell data and the wireless network optimization scheme as positive samples of a wireless network optimization model.

[0061] That is to say, in the embodiment of the present disclosure, when the retest result indicates that the wireless network optimization scheme has completed network optimization, it can be determined that the wireless network optimization scheme is suitable for the current scenario, and therefore the network test data, base station cell data and wireless network optimization scheme in the scenario can be used as positive samples of the wireless network optimization model, and the wireless network optimization model is iteratively trained to ensure that the wireless network optimization model is suitable for individual application scenarios.

[0062] S203: If the retest result indicates that the wireless network optimization scheme has not completed network optimization, the wireless network optimization scheme is rolled back.

[0063] That is to say, in the embodiment of the present disclosure, when the retest result indicates that the wireless network optimization scheme has not completed network optimization, the wireless network optimization scheme is rolled back, so as to prompt the user to formulate other network optimization schemes, thereby ensuring the robustness of the network optimization process.

[0064] In the embodiment, by receiving the retest result of the external device, if the retest result indicates that the wireless network optimization scheme has completed network optimization, the network test data, base station cell data and wireless network optimization scheme are used as positive samples of the wireless network optimization model, and if the retest result indicates that the wireless network optimization scheme has not completed network optimization, the wireless network optimization scheme is rolled back. In this way, reliable data support can be provided for the iterative optimization process of the wireless network optimization model based on the retest result, and the wireless network optimization scheme is rolled back in time when the retest result indicates that the wireless network optimization scheme has not completed network optimization, so as to facilitate the user to formulate other network optimization methods, thereby ensuring the robustness of the wireless network optimization process.

[0065] Figure 3 is a flowchart of a method for generating a wireless network optimization scheme according to another embodiment of the present disclosure.

[0066] As shown in Figure 3 the method for generating a wireless network optimization scheme is performed by an external device, and the method comprises:

[0067] S301: Obtain log data of a terminal and parse the log data to obtain network test data.

[0068] The log data can be a log file composed of network test data of the terminal, including but not limited to signaling, events, latitude and longitude, RSRP, SINR, MCS, rate, neighboring cells, etc., without limitation.

[0069] The network test data can be terminal test data related to network optimization obtained by parsing the log data.

[0070] In the embodiments of the present disclosure, the external device can be connected to the user terminal through the interface (including but not limited to Micro interface, Type C interface, Lightning, etc.) of the transmission module, so as to realize data acquisition on the terminal side.

[0071] In the embodiments of the present disclosure, when the log data of the terminal is acquired by the external device, and the log data is parsed to obtain the network test data, the data support of the terminal side can be provided for subsequent generation of the reporting data.

[0072] S302: Collecting base station cell data from the network optimization platform according to the network test data.

[0073] The base station cell data can be data related to the cell base station collected and acquired by the network optimization platform.

[0074] That is, in the embodiments of the present disclosure, after the external device acquires the log data of the terminal, and parses the log data to obtain the network test data, the network test data can be used as a retrieval basis to acquire the corresponding base station cell data from the network optimization platform, so as to provide data support of the network optimization platform side for subsequent generation of the reporting data.

[0075] S303: Generating and sending the reporting data to the base station according to the network test data and the base station cell data.

[0076] That is, in the embodiments of the present disclosure, after the external device obtains the network test data and the base station cell data, the reporting data can be generated and sent to the base station in combination with the network test data and the base station cell data, so as to provide the required data for the wireless network optimization model of the base station side.

[0077] S304: Receiving the wireless network optimization scheme sent by the base station, wherein the wireless network optimization scheme is acquired by the base station based on the wireless network optimization model processing the reporting data.

[0078] That is, in the embodiments of the present disclosure, the base station can be used as an edge computing server node to be responsible for model training and reasoning, and to generate the corresponding wireless network optimization scheme based on the reporting data generated by the external device.

[0079] In this embodiment, the network test data is obtained by acquiring log data of the terminal and analyzing the log data, the base station cell data is obtained from the network optimization platform according to the network test data, the reporting data is generated and sent to the base station according to the network test data and the base station cell data, and the wireless network optimization scheme sent by the base station is received, wherein the wireless network optimization scheme is obtained by the base station based on the wireless network optimization model processing the reporting data. The wireless network optimization scheme corresponding to the reporting data collected by the external device can be obtained based on the wireless network optimization model pre-trained on the base station side, which can effectively improve the intelligent degree of the network optimization process, accurately locate the network problem, and improve the network optimization efficiency.

[0080] Optionally, in some embodiments, the network test data includes a data report and an event report, and the base station cell data at least includes a neighbor relation table of a mobile communication network, MRO, MDT, a cell performance table, a cell parameter configuration table, a work parameter, an alarm, and a historical optimization scheme; wherein,

[0081] The field variable identifier of the data report at least includes: cell, time, longitude, latitude, signal receiving power, signal-to-noise ratio, frequency point, MAC layer downlink rate, MAC layer uplink rate, downlink modulation and coding strategy, uplink modulation and coding strategy, block error rate, physical cell identifier, and voice packet sequence number;

[0082] The field variable identifier of the event report at least includes: A1 event of closing inter-frequency measurement, A2 event of starting inter-frequency measurement, A3 event of handover, A5 event of handover based on a main service cell and a neighbor cell threshold, B1 event of inter-system handover, B2 event of handover based on a main service cell and an inter-system neighbor cell threshold, handover failure, radio link failure, voice drop, and RRC connection reestablishment failure.

[0083] Optionally, in some embodiments, the reporting data is generated based on the following manner: wireless network problem statistics are performed on the data report, the event report, and the neighbor relation table based on the cell and the time to obtain a wireless network problem statistics table; the wireless network problem statistics table is associated with the MRO, the MDT, the cell performance table, the cell parameter configuration table, the work parameter, the alarm, and the historical optimization scheme based on the cell and the time to obtain the reporting data.

[0084] Figure 4 is a flow diagram of a method for generating a wireless network optimization scheme according to another embodiment of the present disclosure.

[0085] As shown in Figure 4 , the method for generating a wireless network optimization scheme is performed by an external device, and the method comprises:

[0086] S401: sending the wireless network optimization scheme to an operation and maintenance center.

[0087] The operation and maintenance center OMC can be used to monitor and manage the running state, fault elimination, performance analysis and the like of the wireless network device and system.

[0088] Optionally, in some embodiments, the wireless network optimization scheme can be audited and confirmed by a user before being sent to the operation and maintenance center, and the wireless network optimization scheme can be sent to the operation and maintenance center after being executed, so as to ensure the reliability of the wireless network optimization scheme.

[0089] That is to say, in the embodiments of the present disclosure, the external device can send the wireless network optimization scheme to the operation and maintenance center for execution after receiving the wireless network optimization scheme.

[0090] S402: receiving an execution result of the operation and maintenance center for the wireless network optimization scheme.

[0091] The execution result can be used to indicate the execution progress of the operation and maintenance center for the wireless network optimization scheme.

[0092] That is to say, in the embodiments of the present disclosure, the execution progress of the wireless network optimization scheme can be monitored in real time, thereby providing a reliable execution basis for subsequent retesting.

[0093] S403: when the execution result indicates that the wireless network optimization scheme has been completely executed, obtaining new log data of the terminal, and obtaining a retest result according to the new log data, and sending the retest result to the base station, wherein the retest result is used to indicate whether the wireless network optimization scheme completes network optimization.

[0094] That is to say, in the embodiments of the present disclosure, after the wireless network optimization scheme is executed, the wireless network quality of the terminal can be retested, so as to determine the execution effect of the wireless network optimization scheme.

[0095] In the embodiments, by sending the wireless network optimization scheme to the operation and maintenance center, receiving the execution result of the operation and maintenance center for the wireless network optimization scheme, when the execution result indicates that the wireless network optimization scheme has been completely executed, obtaining new log data of the terminal, and obtaining a retest result according to the new log data, and sending the retest result to the base station, wherein the retest result is used to indicate whether the wireless network optimization scheme completes network optimization. Thus, after determining the wireless network optimization scheme, the wireless network optimization scheme can be executed and evaluated, so as to determine the corresponding measures according to different retest results, which can effectively improve the robustness of the wireless network optimization process.

[0096] Based on the above embodiment, the present disclosure proposes a method for recommending a wireless network optimization scheme based on base station edge cloud computing. The method includes the following steps: analyzing the user network test LOG file collected by the mobile terminal through a newly added process, correlating the data with the base station cell performance, parameters, alarms, problem solving history scheme tags, and other data collected and sent by the network optimization platform to the newly added external device, sending the correlated data to the base station edge cloud computing server through the newly added process, constructing a wireless network problem optimization recommendation model, generating a wireless network problem optimization recommendation scheme, and sending the scheme to the external device through the newly added process. After manual confirmation, the parameter adjustment scheme is converted into an executable instruction, which is sent to the operation and maintenance center OMC through VPN connection for execution. The execution result is fed back to the external device. Finally, the problem is confirmed to be solved through retesting, and the model sample iteration is realized. The specific steps are as follows Figure 5 Figure 5 The flowchart of the method for recommending a wireless network optimization scheme based on base station edge cloud computing according to the present disclosure is shown.

[0097] The newly added external device has the following functions:

[0098] 1. Power module: store power for easy carrying of the external device;

[0099] 2. Storage module: save and manage external device data, including but not limited to LOG, cell configuration parameter table, and optimization scheme;

[0100] 3. Transmission module: has virtual private network connection, WIFI connection, Bluetooth connection, and is used for connecting external devices including but not limited to test terminal, OMC, base station, etc.

[0101] 4. Acquisition module: acquires test terminal data including but not limited to signaling, events, latitude and longitude, RSRP, SINR, etc. through interfaces including but not limited to Micro interface, Type C interface, Lightning interface;

[0102] 5. Operation module: supports APP software operation and analysis of LOG, and performs not too complex operation.

[0103] The wireless network optimization method recommendation process mainly includes the following steps:

[0104] (1) External device acquires UE test data

[0105] ​When the external device is connected to the user terminal through the interface of the transmission module (including but not limited to Micro interface, Type C interface, Lightning), the external device acquisition module acquires the test data of the UE, including but not limited to signaling, events, latitude, longitude, RSRP, SINR, MCS, rate, neighboring cells and other information, and stores them in the storage module.

[0106] (2) External device analyzes UE test data

[0107] After the collection is completed, the external device parses the LOG through the operation module to obtain data reports and event reports including but not limited to the main service cell (4G represented by ECI or 5G represented by NCI), RSRP, SINR, MCS, rate, etc., and sends the data to the network optimization platform.

[0108] The field variable identification of the data report generated by the external device operation module parsing the LOG includes but is not limited to cell, time (TIME), longitude (Lon), latitude (Lat), signal receiving power (RSRP), signal-to-noise ratio (SINR), frequency point (Arfcn), MAC layer downlink rate (MAC DL), MAC layer uplink rate (MAC UL), downlink modulation and coding strategy (MCS DL), uplink modulation and coding strategy (MCS UL), block error rate (BLER), physical cell identifier (PCI), and voice packet sequence number (Sequence Number).

[0109] The field variable identification of the event report generated by the external device operation module parsing the LOG includes but is not limited to A1 event (EventA1) for closing inter-frequency measurement, A2 event (EventA2) for starting inter-frequency measurement, A3 event (EventA3) for handover, A5 event (EventA5) for handover based on main service cell and neighboring cell threshold, B1 event (EventB1) for inter-system handover, B2 event (EventB2) for handover based on main service cell and inter-system neighboring cell threshold, handover failure (HO fail), radio link failure (Radio Link Fail), call drop, RRC connection reestablishment failure (RRC Reestablishment Fail).

[0110] (3) External device collects network optimization platform data

[0111] Based on the data reports and event reports sent by the external device, the platform data collected includes but is not limited to MRO, MDT, cell performance table, cell parameter configuration table, work parameters, neighboring cell relationship table, alarms, historical optimization schemes, etc. of the mobile communication network (4G, 5G, etc.), and the platform data is pushed to the external device storage module.

[0112] (4) Multidimensional data reporting to base station edge server

[0113] The newly added external device collects data reports, event reports, and neighbor cell relationship tables, and statistically analyzes wireless network problems (including but not limited to weak coverage, overlapping coverage, missing neighbor cell configuration, frequent handover, low data rate, dropped calls, etc.) according to ECI / NCI (cell ID) and TIME (time), generating a wireless network problem statistics table. This table is then linked with configuration parameters, performance indicators, alarms, and historical optimization schemes (including adjusting neighbor cell CIO, adjusting OFFSET, RF adjustments, etc.) via ECI / NCI and TIME (time), converted into Boolean data according to assignment rules, generating multi-dimensional data, and uploaded to the base station edge server via mobile communication networks (4G, 5G, etc.).

[0114] (5) Build a wireless network optimization model on the edge server

[0115] 5G networks natively adopt cloud-based construction, with the wireless access network serving as an edge cloud. Therefore, base stations can be used as edge computing nodes. A new process enables data communication between external devices and base station edge computing nodes. External devices send data required for the wireless network optimization model, while the base station edge computing nodes handle model training and application. They can also control external devices, enabling functions such as powering on / off and network shutdown. Figure 6 As shown, Figure 6 This is a schematic diagram of the structure between the terminal and the base station edge computing node as proposed in this disclosure.

[0116] The detailed modeling process of the wireless network optimization model can be described as follows: Figure 7 As shown, Figure 7 This is a flowchart illustrating the construction and application of the wireless network optimization model proposed in this disclosure, which includes the following steps:

[0117] (5.1) Construction of training samples

[0118] After receiving data reported by external devices (including but not limited to data reports, event reports, configuration parameters, performance indicators, alarms, and historical optimization schemes), the base station edge node server obtains a multi-dimensional data table. Combining the historical optimization schemes and their retest data, it outputs the optimal optimization scheme and constructs the training data for the model. The training sample data for the model is as follows:

[0119] [CAUSE1, CAUSE2, CAUSE3,...CAUSEM,Scheme]

[0120] wherein "CAUSE1, CAUSE2, CAUSE3,.... CAUSEM" represents M characteristic data, and "Scheme" represents whether the optimization scheme is feasible. For example, wherein,

[0121] "CAUSE1" can be used to indicate "whether weak coverage, yes for 1, no for 0";

[0122] "CAUSE2" can be used to indicate "whether overlapping coverage, yes for 1, no for 0";

[0123] "CAUSE3" can be used to indicate "whether low SINR, yes for 1, no for 0";

[0124] "CAUSE4" can be used to indicate "whether the neighbor area is missing, yes for 1, no for 0";

[0125] "CAUSE5" can be used to indicate "whether frequent switching, yes for 1, no for 0";

[0126] "CAUSE6" can be used to indicate "whether low rate, yes for 1, no for 0";

[0127] ...

[0128] "Scheme" can be used to indicate "optimization scheme".

[0129] The Scheme label vector corresponds to the optimization scheme, including adjusting the neighbor area CIO, adjusting the OFFSET, radio frequency adjustment, etc.

[0130] (5.2) AI model training

[0131] Machine learning algorithms (including but not limited to lightgbm, xgboost, etc.) are used to input multi-dimensional data, train the model, and obtain a wireless network problem optimization scheme model. As shown in Figure 8 , the wireless network problem optimization scheme model is a training process diagram according to the present disclosure. Figure 8

[0132] (5.3) AI model application

[0133] After the base station edge cloud server completes the model training, input new multi-dimensional data, and output the wireless network problem optimization scheme. If multiple optimization schemes are output, the optimization scheme with the highest usage frequency is used as the optimal scheme.

[0134] The input and output data are as follows:

[0135] 1) Data input: Collect multi-dimensional data in the following format:

[0136] ​[CAUSE1, CAUSE2, CAUSE3,... CAUSEM]

[0137] 2) Data output: the model outputs the optimal wireless network problem optimization scheme.

[0138] (6) Push the optimization scheme to the external device through the mobile network

[0139] After the wireless network problem optimization scheme model outputs the optimal optimization scheme, it is converted into a data format executable by the OMC. At least, it includes variable identifiers such as DistName, VERSION, eNB / gNBS, CELLID, NCELL, and Operation. (DistName is the path, VERSION is the base station version number, eNB / gNBS is the serving cell station number, CELLID is the cell number, NCELL is the corresponding neighbor cell for parameter adjustment, and Operation is divided into creat, delete, and update.)

[0140] (7) Optimization scheme confirmation execution

[0141] If the wireless network problem optimization scheme is confirmed to be correct, the external device converts the parameter adjustment table into a format file (work parameter adjustment table needs to be fed back to the wireless network optimization center to go through the adjustment process, to be executed by the tower worker in the field) matching the manufacturer's network management, such as XML. The external device connects to the OMC through VPN and transmits the optimization scheme in the related format (including but not limited to XML) executable by the OMC to the OMC for execution based on the interface (for example: restful interface). If the wireless network problem optimization scheme is confirmed to have problems and is not executed, the process is ended and manual processing is performed.

[0142] (8) Optimization scheme execution feedback

[0143] After the OMC is executed, the execution result is fed back to the external device through the interface (for example: restful interface). When the optimization scheme is completely implemented (including parameter and work parameter adjustment), the effect of the optimization scheme is tested and confirmed again. If there is an effect, it is taken as a positive sample.

[0144] (9) Scheme retest effect confirmation

[0145] The same wireless network problem point area is retested, and after obtaining the LOG, the external device operation module parses the LOG to obtain data reports and event reports, and obtains multi-dimensional data through the association table. If the problem root cause is not located, the retest is normal, and the related data is fed back to the wireless network optimization model as a positive sample. If the problem root cause is located, the wireless network optimization scheme is rolled back, and manual processing is performed.

[0146] Figure 9is a structural schematic diagram of a wireless network optimization scheme generation device according to an embodiment of the present disclosure.

[0147] As shown in Figure 9 The wireless network optimization scheme generation device 90 is executed by a base station and includes:

[0148] The first receiving module 901 is configured to receive report data of an external device, where the report data is used to indicate network test data and base station cell data, the network test data is obtained by the external device according to log data of a terminal, and the base station cell data is obtained by the external device from a network optimization platform according to the network test data.

[0149] The first sending module 902 is configured to input the report data into a pre-trained wireless network optimization model to obtain a wireless network optimization scheme, and send the wireless network optimization scheme to the external device.

[0150] It should be noted that the foregoing explanation and description of the wireless network optimization scheme generation method also apply to the wireless network optimization scheme generation device of this embodiment, which will not be described here again.

[0151] In this embodiment, the report data of the external device is received, where the report data is used to indicate network test data and base station cell data, the network test data is obtained by the external device according to log data of a terminal, and the base station cell data is obtained by the external device from a network optimization platform according to the network test data. The report data is input into a pre-trained wireless network optimization model to obtain a wireless network optimization scheme, and the wireless network optimization scheme is sent to the external device. By implementing the method of the present disclosure, the report data collected by the external device can be processed based on the pre-trained wireless network optimization model on the base station side to obtain a corresponding wireless network optimization scheme, which can effectively improve the intelligent degree of the network optimization process, accurately locate network problems, and improve the network optimization efficiency.

[0152] Figure 10 is a structural schematic diagram of a wireless network optimization scheme generation device according to another embodiment of the present disclosure.

[0153] As shown in Figure 10 The wireless network optimization scheme generation device 100 is executed by an external device and includes:

[0154] The first obtaining module 1001 is configured to obtain log data of a terminal, and analyze the log data to obtain network test data.

[0155] The second obtaining module 1002 is configured to obtain base station cell data from a network optimization platform according to the network test data.

[0156] The second sending module 1003 is used to generate and send reporting data to the base station based on network test data and base station cell data;

[0157] The second receiving module 1004 is used to receive the wireless network optimization scheme sent by the base station, wherein the wireless network optimization scheme is obtained by the base station processing and reporting data based on the wireless network optimization model.

[0158] It should be noted that the foregoing explanation of the method for generating wireless network optimization schemes also applies to the wireless network optimization scheme generation apparatus of this embodiment, and will not be repeated here.

[0159] In this embodiment, it is passed. Figure 11 A block diagram of an exemplary electronic device suitable for implementing embodiments of the present disclosure is shown. Figure 11 The electronic device 12 shown is merely an example and should not impose any limitation on the functionality and scope of use of the embodiments disclosed herein.

[0160] like Figure 11 As shown, the electronic device 12 is represented in the form of a general-purpose computing device. The components of the electronic device 12 may include, but are not limited to: one or more processors or processing units 16, system memory 28, and bus 18 connecting different system components (including system memory 28 and processing unit 16).

[0161] Bus 18 represents one or more of several bus architectures, including a memory bus or memory controller, a peripheral bus, a graphics acceleration port, a processor, or a local bus using any of the various bus architectures. Examples of these architectures include, but are not limited to, the Industry Standard Architecture (ISA) bus, the Micro Channel Architecture (MAC) bus, the Enhanced ISA bus, the Video Electronics Standards Association (VESA) local bus, and the Peripheral Component Interconnect (PCI) bus.

[0162] Electronic device 12 typically includes a variety of computer system readable media. These media can be any available media that can be accessed by electronic device 12, including volatile and non-volatile media, removable and non-removable media.

[0163] Memory 28 may include computer system readable media in the form of volatile memory, such as Random Access Memory (RAM) 30 and / or cache memory 32. Electronic device 12 may further include other removable / non-removable, volatile / non-volatile computer system storage media. By way of example only, storage system 34 may be used to read and write non-removable, non-volatile magnetic media (… Figure 11 Not shown; usually referred to as a "hard drive".

[0164] although Figure 11 Not shown, a disk drive for reading and writing to a removable non-volatile disk (e.g., a "floppy disk") and an optical disc drive for reading and writing to a removable non-volatile optical disc (e.g., a compact disc read-only memory (CD-ROM), a digital video disc read-only memory (DVD-ROM), or other optical media) may be provided. In these cases, each drive may be connected to bus 18 via one or more data media interfaces. Memory 28 may include at least one program product having a set (e.g., at least one) of program modules configured to perform the functions of the embodiments of this disclosure.

[0165] A program / utility 40 having a set (at least one) of program modules 42 may be stored, for example, in memory 28. Such program modules 42 include, but are not limited to, an operating system, one or more application programs, other program modules, and program data. Each or some combination of these examples may include an implementation of a network environment. Program modules 42 typically perform the functions and / or methods described in the embodiments of this disclosure.

[0166] The electronic device 12 can also communicate with one or more external devices 14 such as a keyboard or pointing devices, a display 24, etc.; other devices such as portable memory storage devices; and / or to one or more devices 26 that enable

[0167] The processing unit 16 performs various general processing functions, preferably at the direction of software applications 30.

[0168] To implement the above embodiments, the present disclosure further provides a non-transitory computer readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the method for generating a wireless network optimization scheme as proposed in the above embodiments of the present disclosure.

[0169] To implement the above embodiments, the present disclosure further provides a computer program product, which, when executed by an instruction processing unit, performs the method for generating a wireless network optimization scheme as proposed in the above embodiments of the present disclosure.

[0170] Other embodiments of the present disclosure will be apparent to those skilled in the art from consideration of the specification and practice of the features disclosed herein. The present disclosure is intended to cover any and all variations of the present disclosure comprising features that are within the true spirit and scope of the present disclosure. It is submitted that the true scope of the present disclosure is indicated by the appended claims, and that the specification and examples are merely exemplary of the present disclosure.

[0171] It is to be understood that the present disclosure is not limited to the precise construction described and as shown in the drawings, and that various modifications and changes can be made by those skilled in the art without departing from the scope of the present disclosure. The scope of the present disclosure is limited only by the claims that follow.

[0172] It should be noted that in the description of the present disclosure, the terms "first", "second", etc. are only used for descriptive purposes and cannot be understood as indicating or implying relative importance. In addition, in the description of the present disclosure, the meaning of "multiple" is two or more, unless otherwise stated.

[0173] Any process or method descriptions in flow charts or described elsewhere herein can be understood as representing code modules, segments, or portions of code that include one or more executable instructions for implementing specific logic functions or other processes. The various embodiments of the present disclosure can include additional or fewer steps or processes, and the order of the steps or processes can be changed, including according to the function involved, without departing from the scope of the embodiments of the present disclosure, which should be understood by those skilled in the art to which the embodiments of the present disclosure belong.

[0174] It should be understood that various parts of the present disclosure can be implemented in hardware, software, firmware, or a combination thereof. In the above-described embodiments, a plurality of steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, it can be implemented by any one or a combination of the following technologies known in the art: discrete logic circuit with logic gate circuit for implementing logic functions on data signals, application specific integrated circuit with suitable combination logic gate circuit, programmable gate array (PGA), field programmable gate array (FPGA), etc.

[0175] Those skilled in the art of the present technology can understand that all or part of the steps carried out by the above-mentioned embodiments can be completed by programs instructing related hardware, and the programs can be stored in a computer readable storage medium, which includes one or a combination of steps of the method embodiments when executed.

[0176] In addition, each functional unit in each embodiment of the present disclosure can be integrated into one processing module, or each unit can exist physically alone, or two or more units can be integrated into one module. The above-mentioned integrated module can be realized in the form of hardware or in the form of software functional module. When the integrated module is realized in the form of software functional module and sold or used as an independent product, it can also be stored in a computer readable storage medium.

[0177] The storage medium mentioned above can be a read-only memory, a magnetic disk or an optical disk, etc.

[0178] In the description of the specification, the description of the terms "one embodiment", "some embodiments", "an example", "a specific example", or "some examples" etc. means that the specific features, structures, materials or characteristics described in connection with the embodiment or example are included in at least one embodiment or example of the present disclosure. In the specification, the illustrative expressions of the above terms do not necessarily mean the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described can be combined in any one or more embodiments or examples in a suitable manner.

[0179] Although the embodiments of the present disclosure have been shown and described above, it is understood that the above-described embodiments are exemplary and are not to be construed as limiting the present disclosure, and those skilled in the art can make changes, modifications, replacements and variations to the above-described embodiments within the scope of the present disclosure.

Claims

1. A method for generating a wireless network optimization scheme, characterized in that, The method, executed by the base station, includes: The system receives data reported by an external device, wherein the reported data is used to indicate network test data and base station cell data. The network test data is obtained by the external device through parsing the terminal's log data, and the base station cell data is obtained by the external device from the network optimization platform based on the network test data. The reported data is input into a pre-trained wireless network optimization model to obtain a wireless network optimization scheme, and the wireless network optimization scheme is sent to the external device. The network test data includes data reports and event reports, and the base station cell data includes at least the mobile communication network's neighbor cell relationship table, MRO, MDT, cell performance table, cell parameter configuration table, operating parameters, alarms, and historical optimization schemes; wherein, The data report's field variable identifiers include at least: cell, time, longitude, latitude, signal received power, signal-to-noise ratio, frequency, MAC layer downlink rate, MAC layer uplink rate, downlink modulation and coding strategy, uplink modulation and coding strategy, block error rate, physical cell identifier, and voice packet sequence number. The field variable identifiers of the event report include at least the following: inter-frequency measurement shutdown A1 event, inter-frequency measurement startup A2 event, handover A3 event, handover A5 event based on primary serving cell and neighboring cell thresholds, inter-system handover B1 event, handover B2 event based on primary serving cell and inter-system neighboring cell thresholds, handover failure, radio link failure, voice call drop, and RRC connection reconstruction failure. The wireless network problem statistics table in the reported data is associated with the MRO, MDT, cell performance table, cell parameter configuration table, operating parameters, alarms, and historical optimization schemes based on the cell and the time. The wireless network problem statistics table is obtained by the external device from the data report, the event report, and the neighbor cell relationship table based on the cell and the time.

2. The method as described in claim 1, characterized in that, Also includes: Receive the retest results from the external device; If the retest results indicate that the wireless network optimization scheme has completed network optimization, then the network test data, the base station cell data, and the wireless network optimization scheme are used as positive samples of the wireless network optimization model. If the retest results indicate that the wireless network optimization scheme has not completed network optimization, then the wireless network optimization scheme shall be rolled back.

3. A method for generating a wireless network optimization scheme, characterized in that, The method, executed by an external device, includes: Obtain the terminal's log data and parse the log data to obtain network test data; Base station cell data are collected from the network optimization platform based on the network test data. Based on the network test data and the base station cell data, generate and send reporting data to the base station; The system receives a wireless network optimization scheme sent by the base station, wherein the wireless network optimization scheme is obtained by the base station processing the reported data based on the wireless network optimization model. The network test data includes data reports and event reports, and the base station cell data includes at least the mobile communication network's neighbor cell relationship table, MRO, MDT, cell performance table, cell parameter configuration table, operating parameters, alarms, and historical optimization schemes; wherein, The data report's field variable identifiers include at least: cell, time, longitude, latitude, signal received power, signal-to-noise ratio, frequency, MAC layer downlink rate, MAC layer uplink rate, downlink modulation and coding strategy, uplink modulation and coding strategy, block error rate, physical cell identifier, and voice packet sequence number. The field variable identifiers of the event report include at least the following: inter-frequency measurement shutdown A1 event, inter-frequency measurement startup A2 event, handover A3 event, handover A5 event based on primary serving cell and neighboring cell thresholds, inter-system handover B1 event, handover B2 event based on primary serving cell and inter-system neighboring cell thresholds, handover failure, radio link failure, voice call drop, and RRC connection reconstruction failure. The reported data is generated in the following manner: Based on the cell and the time, perform wireless network problem statistics on the data report, the event report and the neighbor cell relationship table to obtain a wireless network problem statistics table; The reported data is obtained by associating the wireless network problem statistics table with the MRO, MDT, cell performance table, cell parameter configuration table, operating parameters, alarms, and historical optimization schemes based on the cell and the time.

4. The method as described in claim 3, characterized in that, Also includes: Send the wireless network optimization plan to the operations and maintenance center; Receive the execution results of the wireless network optimization scheme from the operation and maintenance center; When the execution result indicates that the wireless network optimization scheme has been fully executed, new log data of the terminal is obtained, and the retest result is obtained by parsing the new log data. The retest result is then sent to the base station, wherein the retest result is used to indicate whether the wireless network optimization scheme has completed network optimization.

5. A device for generating a wireless network optimization scheme, characterized in that, The device, executed by the base station, includes: The first receiving module is used to receive data reported by an external device, wherein the reported data is used to indicate network test data and base station cell data, the network test data is obtained by the external device through parsing the terminal's log data, and the base station cell data is obtained by the external device from the network optimization platform based on the network test data; The first sending module is used to input the reported data into a pre-trained wireless network optimization model to obtain a wireless network optimization scheme, and send the wireless network optimization scheme to the external device; The network test data includes data reports and event reports, and the base station cell data includes at least the mobile communication network's neighbor cell relationship table, MRO, MDT, cell performance table, cell parameter configuration table, operating parameters, alarms, and historical optimization schemes; wherein, The data report's field variable identifiers include at least: cell, time, longitude, latitude, signal received power, signal-to-noise ratio, frequency, MAC layer downlink rate, MAC layer uplink rate, downlink modulation and coding strategy, uplink modulation and coding strategy, block error rate, physical cell identifier, and voice packet sequence number. The field variable identifiers of the event report include at least the following: inter-frequency measurement shutdown A1 event, inter-frequency measurement startup A2 event, handover A3 event, handover A5 event based on primary serving cell and neighboring cell thresholds, inter-system handover B1 event, handover B2 event based on primary serving cell and inter-system neighboring cell thresholds, handover failure, radio link failure, voice call drop, and RRC connection reconstruction failure. The wireless network problem statistics table in the reported data is associated with the MRO, MDT, cell performance table, cell parameter configuration table, operating parameters, alarms, and historical optimization schemes based on the cell and the time. The wireless network problem statistics table is obtained by the external device from the data report, the event report, and the neighbor cell relationship table based on the cell and the time.

6. A device for generating a wireless network optimization scheme, characterized in that, Performed by an external device, the device comprising: The first acquisition module is used to acquire the terminal's log data and parse the log data to obtain network test data; The second acquisition module is used to collect and acquire base station cell data from the network optimization platform based on the network test data. The second sending module is used to generate and send reporting data to the base station based on the network test data and the base station cell data; The second receiving module is used to receive the wireless network optimization scheme sent by the base station, wherein the wireless network optimization scheme is obtained by the base station processing the reported data based on the wireless network optimization model; The network test data includes data reports and event reports, and the base station cell data includes at least the mobile communication network's neighbor cell relationship table, MRO, MDT, cell performance table, cell parameter configuration table, operating parameters, alarms, and historical optimization schemes; wherein, The data report's field variable identifiers include at least: cell, time, longitude, latitude, signal received power, signal-to-noise ratio, frequency, MAC layer downlink rate, MAC layer uplink rate, downlink modulation and coding strategy, uplink modulation and coding strategy, block error rate, physical cell identifier, and voice packet sequence number. The field variable identifiers of the event report include at least the following: inter-frequency measurement shutdown A1 event, inter-frequency measurement startup A2 event, handover A3 event, handover A5 event based on primary serving cell and neighboring cell thresholds, inter-system handover B1 event, handover B2 event based on primary serving cell and inter-system neighboring cell thresholds, handover failure, radio link failure, voice call drop, and RRC connection reconstruction failure. The second sending module is specifically used for: Based on the cell and the time, perform wireless network problem statistics on the data report, the event report and the neighbor cell relationship table to obtain a wireless network problem statistics table; The reported data is obtained by associating the wireless network problem statistics table with the MRO, MDT, cell performance table, cell parameter configuration table, operating parameters, alarms, and historical optimization schemes based on the cell and the time.

7. An electronic device, characterized in that, include: At least one processor; as well as A memory communicatively connected to the at least one processor; wherein, The memory stores instructions that can be executed by the at least one processor to enable the at least one processor to perform the method of any one of claims 1-4.

8. A non-transitory computer-readable storage medium storing computer instructions, characterized in that, in, The computer instructions are used to cause the computer to perform the method according to any one of claims 1-4.

9. A computer program product, characterized in that, Includes a computer program that, when executed by a processor, implements the steps of the method according to any one of claims 1-4.

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